Introduction Police employees undertake challenging duties which may adversely impact their health. This study explored the prevalence of and risk factors for probable mental disorders amongst a representative sample of UK police employees. The association between mental illness and alterations in blood pressure was also explored. Methods Data were used from the Airwave Health Monitoring Study which was established to monitor the possible physical health impacts of a new communication system on police employees. Data included sociodemographic characteristics, lifestyle habits, depression, anxiety, and post-traumatic stress disorder (PTSD) symptoms and blood pressure. Descriptive statistics were used to explore the prevalence of probable mental disorders and associated factors. Stepwise linear regression was conducted, controlling for confounding variables, to examine associations between mental disorders and blood pressure. Results The sample included 40,299 police staff, police constable/sergeants and inspectors or above. Probable depression was most frequently reported (9.8%), followed by anxiety (8.5%) and PTSD (3.9%). Groups at risk for probable mental disorders included police staff, and police employees who reported drinking heavily. Police employees exposed to traumatic incidents in the past six months had a doubling in rates of anxiety or depression and a six-fold increase in PTSD compared to those with no recent trauma exposure. Adjusted logistic regression models did not reveal any significant association between probable mental disorders and systolic blood pressure but significantly elevated diastolic blood pressure (≈1mmHg) was found across mental disorders. Conclusions These results show lower rates of probable mental disorders, especially PTSD, than reported in other studies focusing on police employees. Although mental ill health was associated with increased diastolic blood pressure, this was unlikely to be clinically significant. These findings highlight the importance of continued health monitoring of members of the UK police forces, focusing on employees recently exposed to traumatic incidents, heavy drinkers and police staff.

Background: Individuals who conduct disaster relief work overseas are exposed to a variety of traumatic events that can cause distress and trigger psychological illnesses. Identification of which disaster relief workers may be at risk of experiencing psychological distress or mental health disorders is frequently carried out through preemployment or pre-deployment psychological screening. The primary objective of our review was to assess the evidence for pre-employment and pre-deployment psychological screening of relief workers who work in disaster situations. We aimed to identify specific pre-employment and pre-deployment characteristics that predict impaired wellbeing of an individual following engaging in disaster-related work. Methods: A combined list of search terms was composed relating to disaster-related occupations, screening methods, psychological disorders, and study design. The databases used were PsycINFO, MEDLINE, EMBASE, and GlobalHealth. We included studies that used cross-sectional or longitudinal study designs; were published in the English language in peer-reviewed academic journals; reported on the association between pre-employment and pre-deployment features and post-deployment psychological disorders or distress; considered any occupational groups responding to a specified, discrete crisis; and used at least one validated measure of distress or disorder. We extracted data on the author; year of publication; disaster description; country of study; study design; population sample; disorder(s) outcome and the measures used; and results. Results: Sixty-two, high-quality studies were included in the review. Forty-one potential predictors were identified. Of these, only volunteer status and previous history of mental illness and life stressors emerged as reliable predictors of distress or disorder. Conclusion: The results suggest that whilst it is attractive to screen for pre-employment and pre-deployment indicators of resilience, the evidence base for doing so is weak. At best, this sort of screening can only weakly suggest vulnerability and at worst may result in discrimination. Until better evidence about its usefulness becomes available, employers should exercise caution over its use.

Background Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there is no national marker for identifying those who have previously served in the Armed Forces, making analysis of the health and well-being of veterans using EHRs difficult. Objective This study aimed to develop a tool to identify veterans from free-text clinical documents recorded in a psychiatric EHR database. Methods Veterans were manually identified using the South London and Maudsley (SLaM) Biomedical Research Centre Clinical Record Interactive Search—a database holding secondary mental health care electronic records for the SLaM National Health Service Foundation Trust. An iterative approach was taken; first, a structured query language (SQL) method was developed, which was then refined using natural language processing and machine learning to create the Military Service Identification Tool (MSIT) to identify if a patient was a civilian or veteran. Performance, defined as correct classification of veterans compared with incorrect classification, was measured using positive predictive value, negative predictive value, sensitivity, F1 score, and accuracy (otherwise termed Youden Index). Results A gold standard dataset of 6672 free-text clinical documents was manually annotated by human coders. Of these documents, 66.00% (4470/6672) were then used to train the SQL and MSIT approaches and 34.00% (2202/6672) were used for testing the approaches. To develop the MSIT, an iterative 2-stage approach was undertaken. In the first stage, an SQL method was developed to identify veterans using a keyword rule–based approach. This approach obtained an accuracy of 0.93 in correctly predicting civilians and veterans, a positive predictive value of 0.81, a sensitivity of 0.75, and a negative predictive value of 0.95. This method informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. Conclusions The MSIT has the potential to be used in identifying veterans in the United Kingdom from free-text clinical documents, providing new and unique insights into the health and well-being of this population and their use of mental health care services.

No abstract